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AI in clinical practice : A guide to artificial intelligence and digital medicine
Explains how artificial intelligence is applied to medicine, illustrating not only its enormous potential but also ancillary issues and the limits and risks inherent in its use on a large scale. The book focuses on the intersection between medicine and AI and its implications on the impact of human health care delivery. Topics discussed include wearable devices, health data, Internet of Things, virtual reality, robotic assistance system, and digital intelligence in the health sector. Additionally, sections discuss diagnostics and decision-making systems and machine/deep learning in clinical setting.
Magnetic nanoparticles in human health and medicine : Current medical applications and alternative therapy of cancer
Progress in improving diagnosis by magnetic resonance imaging (MRI) and using non-invasive and non-toxic magnetic nanoparticles for targeted drug delivery. Focusing on cancer diagnosis and therapy, the book covers both fundamental principles and advanced theoretical and experimental research on the magnetic properties, biocompatibilization, biofunctionalization, and application of magnetic nanoparticles in nanobiotechnology and nanomedicine.
Machine learning for neurodegenerative disorders : advancements and applications
Explores the application of machine learning to the understanding, early diagnosis, and management of neurodegenerative disorders. With a specific focus on its role in ongoing clinical trials, the book covers essential topics such as data collection, pre-processing, feature extraction, model development, and validation techniques. It delves into the applications of neuroimaging techniques like magnetic resonance imaging (MRI), computed tomography (CT), and positron emission tomography (PET) in the diagnosis and understanding of neurodegenerative disorders. Additionally, the book examines various machine-learning algorithms employed for biomarker discovery in neurodegenerative disorders. It highlights the role of neuroinformatics and big data analysis in advancing the understanding and management of neurodegenerative disorders. Furthermore, the book reviews future prospects and presents the ethical considerations and regulatory challenges associated with implementing machine learning approaches in the diagnosis, treatment, and prevention of neurodegenerative disorders.
Java for Bioinformatics and Biomedical Applications
Illustrates how individual bioinformatics applications (such as BLAST and Genscan) can be stitched together into a pipeline so that users can direct the output of one tool (for example, gene predictions using Genscan) to perform further analysis (say, homology searching using BLAST).
Chitosan-based nanocomposite materials : fabrication, characterization and biomedical applications
Discovery in the use of chitosan-based nanocomposites in biomedical applications, including the scope to which these novel materials have been incorporated by the community. It provides an exceptional insight into the strategies for the synthesis and chemical modifications of chitosan, characterization techniques, their use as anticancer agents, antimicrobial, antiviral, and antifungal agents, their role in the biomedical field, and applications in drug delivery, gene therapy, dentistry, orthopedics, etc. This book will also emphasize the challenges with previous signs of progress and way for further research, details relating to the current pioneering technology, and future perspectives with a multidisciplinary approach. Furthermore, it presents up-to-date information on the economics, toxicity, and regulations related to these novel materials.
Chitosan : properties and applications in bioengineering
Presents a review of chitosan, including information regarding its structure, main properties, available sources, extraction, and processing techniques. It presents the different applications of chitosan as a biomaterial in fields such as medicine and the agricultural, cosmetology, pharmaceutical, and food industries to have readers obtain a more technical vision of the current use of this biopolymer.
Biomedical applications and toxicity of nanomaterials
Covers the recent trends on the biological applications of nanomaterials, methods for their preparation, and techniques for their characterization. Further, the book examines the fundamentals of nanotoxicity, methods to assess the toxicity of engineered nanomaterials, approaches to reduce toxicity during synthesis. It also provides an overview of the state of the art in the application of Artificial intelligence-based methodologies for evaluation of toxicity of drugs and nanoparticles. The book further discusses nanocarrier design, routes of various nanoparticle administration, nano based drug delivery systems, and the toxicity challenges associated with each drug delivery method. It presents the latest advances in the interaction of nanoparticles with the cellular environment and assess nanotoxicity of these engineered nanoparticles. The book also explores the comparative and mechanistic genotoxicity assessment of the nanomaterials. This book is useful source of information for industrial practitioners, policy makers, and other professionals in the fields of toxicology, medicine, pharmacology, food, and drugs.
Artificial intelligence in drug design
Looks at applications of artificial intelligence (AI), machine learning (ML), and deep learning (DL) in drug design. The chapters in this book describe how AI/ML/DL approaches can be applied to accelerate and revolutionize traditional drug design approaches such as: structure- and ligand-based, augmented and multi-objective de novo drug design, SAR and big data analysis, prediction of binding/activity, ADMET, pharmacokinetics and drug-target residence time, precision medicine and selection of favorable chemical synthetic routes. How broadly are these approaches applied and where do they maximally impact productivity today and potentially in the near future.
Artificial intelligence based cancer nanomedicine : Diagnostics, therapeutics and bioethics
Nanomedicine is evolving with novel drug formulations devised for multifunctional approaches towards diagnostics ad therapeutics. Nanomedicine-based drug therapy is normally explored at a fixed dose. The drug action is time-dependent, dose-dependent and patient-specific. To overcome challenges of nanomedicine testing, artificial intelligence (AI) serves as a helping tool for optimizing the drug and dose parameters. Real time conversions between these two features enables upgradation of patient data acquisition and improved design of nanomaterials. In this scenario, AI-based pattern analysis and algorithms models can greatly improve accuracy of diagnostics and therapeutics.
An introduction to generative drug discovery
Describes the state‑of‑the‑art methods and applications for de novo design of drug candidates using generative chemistry models as well as the ethical aspects of this technology. It will provide a foundation for those new to the field as well as those that may already have some experience of its utility. With contributions from scientists in both academia and industry ‘Introduction to Generative Drug Discovery’ may represent one of the earliest if not the first book to focus on this topic.
AI in disease detection : Advancements and applications
Discusses the integration of artificial intelligence to revolutionize disease detection approaches, with case studies of AI in disease detection as well as insight into the opportunities and challenges of AI in healthcare as a whole. The book explores a wide range of individual AI components such as computer vision, natural language processing, and machine learning as well as the development and implementation of AI systems for efficient practices in data collection, model training, and clinical validation.
AI in clinical medicine : A practical guide for healthcare professionals
A Practical Guide for Healthcare Professionals is the definitive reference book for the emerging and exciting use of AI throughout clinical medicine. AI in Clinical Medicine: A Practical Guide for Healthcare Professionals is divided into four sections. Section 1 provides readers with the basic vocabulary that they require, a framework for AI, and highlights the importance of robust AI training for physicians. Section 2 reviews foundational ideas and concepts, including the history of AI. Section 3 explores how AI is applied to specific disciplines. Section 4 describes emerging trends, and applications of AI in medicine in the future.
Machine learning for biomedical application
Biomedicine is a multidisciplinary branch of medical science that consists of many scientific disciplines, e.g., biology, biotechnology, bioinformatics, and genetics; moreover, it covers various medical specialties. In recent years, this field of science has developed rapidly. This means that a large amount of data has been generated, due to (among other reasons) the processing, analysis, and recognition of a wide range of biomedical signals and images obtained through increasingly advanced medical imaging devices. The analysis of these data requires the use of advanced IT methods, which include those related to the use of artificial intelligence, and in particular machine learning. It is a summary of the Special Issue “Machine Learning for Biomedical Application”, briefly outlining selected applications of machine learning in the processing, analysis, and recognition of biomedical data, mostly regarding biosignals and medical images.
Computational analysis and deep learning for medical care : Principles, methods, and applications
Focuses on the sophisticated methods for improving dye extraction and dyeing properties which will minimize the use of bioresource products. This book also brings out the innovative ways of wet chemical processing to alleviate the environmental impacts arising from this sector.
Artificial intelligence in medicine
Provides a structured and analytical guide to the use of artificial intelligence in medicine. Covering all areas within medicine, the chapters give a systemic review of the history, scientific foundations, present advances, potential trends, and future challenges of artificial intelligence within a healthcare setting.
Artificial intelligence in dentistry
Provides an introduction to next-generation applications and technologies for improving diagnostic accuracy and prediction of treatment outcomes in dentistry through the use of artificial intelligence (AI) and machine learning (ML). The authors attempt to bridge the gap between dental research and global health outcomes, as well as provide a comprehensive guide to general and clinical aspects of dental and oral health issues and the etiology, prevalence, assessment, and management of these conditions.
Mathematical Modeling of Biological Systems ; Vol. I : Cellular Biophysics, Regulatory Networks, Development, Biomedicine, and Data Analysis
This two-volume, interdisciplinary work is a unified presentation of a broad range of state-of-the-art topics in the rapidly growing field of mathematical modeling in the biological sciences. Highlighted throughout both works are mathematical and computational approaches to examine central problems in the life sciences, ranging from the organizational principles of individual cells to the dynamics of large populations.
Biophotonics
More profound understanding of the nature of light and light-matter interactions in biology has enabled many applications in the biology and medical fields. So a new discipline is born, namely biophotonics. The aim of this book is to review the current state-of-the-art of the field by means of authoritative chapters written by the world leaders of the respective fields. Biosensors, biochips, optical tomography, optical microsurgery, photodynamics therapy, bioactivation of gene, photobiology of skin, and nanobiophotonics are each introduced and recent advances presented. This book will be useful not only to physicians, biologists, physicists, chemists, materials scientists, and engineers but also to graduate students who are interested in these rapidly developing fields.
Biomineralization : From molecular and nano-structural analyses to environmental science
Over the past 45 years, biomineralization research has unveiled details of the characteristics of the nano-structure of various biominerals; the formation mechanism of this nano-structure, including the initial stage of crystallization; and the function of organic matrices in biominerals, and this knowledge has been applied to dental, medical, pharmaceutical, materials, agricultural and environmental sciences and paleontology. As such, biomineralization is an important interdisciplinary research area, and further advances are expected in both fundamental and applied research.
BioMEMS and Biomedical Nanotechnology : Vol. I: Biological and Biomedical Nanotechnology
Abe Lee has been working on micro/ and nanotechnology for biomedical and biotech applications since 1992. His recent research focuses on the development of integrated micro and nano fluidic chip processors for the following applications: point-of-care diagnostics, "smart" nanomedicine for early detection and treatment, stem cell biology and therapeutics, the synthesis of novel and pure materials, and biosensors to detect environmental and terrorism threats. Jim Lee's research interest includes BioMEMS/NEMS, and polymer micro/nanotechnology. In the last 4 years, he has over 20 refereed journal publications, 2 book chapters, and 5 patents in these areas. He is now leading an NSF Nanoscale Science and Engineering Center for Affordable Nanoengineering of Polymer Biomedical Devices at OSU.
